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AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data

Summary: AutoTSAD unsupervisedly ensembles diverse subsequence detectors to produce anomaly scores for time series without training data. Automated configuration and ensembling match tuned detectors and beat prior selection baselines across diverse anomaly types. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13517
Venue
VLDB
Year
2024
Pagerank
5.0670573e-05
Overall Rank
6,423 | 55.32%
DOI
10.14778/3681954.3681978

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